Quantum Support Vector Machines

The Support Vector Classifier (SVC) and Quantum Support Vector Classifier (QSVC) are the first models that will be used to look at our synthetic dataset, and we will see how a quantum algorithm versus a classical algorithm can work to find potential defaulters. One of the most widely used techniques is known as Support Vector Machines (SVM) (Hearst et al., 1998), which make use of hyperplanes in order to find separable spaces within our data regime. These hyperplanes are responsible for separating our N-dimensional information into different spaces, trying to maximize the margin between samples from the regions split by the hyperplane itself. By softening this margin constraint and allowing some samples to be misclassified, we allow the model to generalize from the dataset itself. This softened version is what we will call an SVC.

Thanks to the abstraction level that Python libraries such as scikit-learn provide, its usage is as simple as calling...